I would highly recommend this paper where the authors describe an
alternative to arbitrary similarity cutoffs

https://pubs.acs.org/doi/pdf/10.1021/ci7004498

Pat

On Wed, Jul 4, 2018 at 9:31 AM Maciek Wójcikowski <mac...@wojcikowski.pl>
wrote:

> Hi
>
> As Nils has mentioned this is fingerprint dependent. ECFP4 have the
> significant cutoff ~0.4, see https://pubs.acs.org/doi/10.1021/ci7004498
>
> ----
> Pozdrawiam,  |  Best regards,
> Maciek Wójcikowski
> mac...@wojcikowski.pl
>
> 2018-07-04 8:44 GMT+02:00 Nils Weskamp <nils.wesk...@gmail.com>:
>
>> Dear Phuong,
>>
>> unfortunately, there is no generic answer to this question since it is
>> highly dependent on the fingerprint, the type of compounds, your
>> specific application and also your chemical intuition. I can only
>> recommend to test a range of different cutoff values and to see how
>> happy you are with the results.
>>
>> If you have access to a list of analogs that you definitely want to find
>> ("known actives") and a large set of known irrelevant compounds, you
>> might be able to use statistical analyses to derive some kind of
>> "optimal" threshold.
>>
>> If we are talking about path-oriented fingerprints (like the RDKit
>> Chemical Fingerprints) and "normal" drug-like molecules, I would
>> typically go down to 0.70 - 0.75 and then manually weed out false hits.
>>
>> Hope this helps,
>> Nils
>>
>> Am 04.07.2018 um 02:24 schrieb Phuong Chau:
>> > To whom it may concern,
>> >
>> > I was working on finding a group of possible neighbors (similar)
>> > chemicals based on Tanimoto Similarity. I am not sure what is the
>> > optimal cutoff for finding similar chemicals. I searched online and they
>> > said it is 0.85 but there are also many exceptions they mentioned about.
>> > Do you have any suggestions?
>> >
>> > Thank you so much for your help
>> >
>> >
>> >
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